2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW) (2016)
May 16, 2016 to May 20, 2016
Tok Wang Ling , School of Computing, National University of Singapore, Singapore
Zhong Zeng , School of Computing, National University of Singapore, Singapore
Thuy Ngoc Le , School of Computing, National University of Singapore, Singapore
Mong Li Lee , School of Computing, National University of Singapore, Singapore
Keyword search in XML and relational databases (RDB) has gained popularity as it provides a user-friendly way to explore structured data. Existing works on XML and RDB keyword search only rely on the structures of XML/RDB data and/or schemas, and this causes serious problems of returning incomplete answers, meaningless answers and overwhelming answers. In this paper, we identify the problems of existing keyword search methods and point out that the main reason of these problems is due to the unawareness of the Object-Relationship-Attribute (ORA) semantics in XML/RDB. We exploit the ORA semantics in XML and RDB, and capture these semantics by constructing the Object tree for XML, and the Object-Relationship-Mixed (ORM) data graph for RDB, respectively. Based on the Object tree and the ORM data graph, we propose an ORA-Semantics based keyword search in XML and RDB. Our semantic approach can avoid the problems of existing methods and improves the completeness and correctness of keyword search. In addition, we extend the keyword query language to include keywords that match the metadata, i.e., the names of tags in XML and the names of relations and attributes in RDB. These keywords reduce the ambiguities of queries and enable us to infer user' search intention more precisely. Finally, we incorporate aggregate functions and GROUPBY into keyword queries to retrieve statistical information from XML and RDB.
XML, Keyword search, Semantics, Aggregates, Relational databases, Periodic structures
T. W. Ling, Z. Zeng, T. N. Le and M. L. Lee, "ORA-semantics based keyword search in XML and relational databases," 2016 IEEE 32nd International Conference on Data Engineering Workshops (ICDEW), Helsinki, Finland, 2016, pp. 157-160.